Field of the Invention
The present invention relates to the oil industry, and more particularly, to the exploitation of underground reservoirs, such as oil reservoirs or gas storage sites. In particular, the invention pertains to modification of representations of the reservoir, called reservoir models, to make them consistent with the various data collected in the field.
Description of the Prior Art
Optimizing and exploiting an oil deposit rely on a description that is as accurate as possible of the structure, of the petrophysical properties, of the fluid properties, etc., of the deposit being studied. For this, the experts use a computer tool which makes it possible to give an account of these aspects in an approximate manner known as the reservoir model. Such a model constitutes an experimental model of the subsoil, representative both of its structure and of its behavior. Generally, this type of experimental model is represented on a computer, and it is then called a numerical model. A reservoir model comprises a meshing or grid, generally three-dimensional, associated with one or more maps of petrophysical properties (facies, porosity, permeability, saturation, etc). The association assigns values of these petrophysical properties to each of the mesh cells of the grid.
These models, which are well known and widely used in the oil industry, make possible determination of numerous technical parameters relating to the study or exploitation of a reservoir, of hydrocarbons for example. In practice, since the reservoir model is representative of the structure of the reservoir and of its behavior, the engineer uses it for example to determine the areas which are most likely to contain hydrocarbons, the areas in which it may be advantageous/necessary to drill an injection or production well to improve the recovery of the hydrocarbons, the type of tools to be used, the properties of the fluids used and recovered, and so on. These interpretations of reservoir models in terms of “exploitation technical parameters” are well known to the experts. Similarly, the modeling of CO2 storage sites makes it possible to monitor these sites, detect unexpected behaviors and predict the displacement of the injected CO2.
The purpose of a reservoir model is therefore to give the best possible account of all the information that is known concerning a reservoir. A reservoir model is representative when a reservoir simulation provides numerical responses that are very close to the history data that have already been observed. The term “history data” is used to mean the production data obtained from measurements on the wells in response to the production of the reservoir (production of oil, production of water from one or more wells, gas/oil ratio (GOR), proportion of production water (“water cut”), and/or repetitive sismic data (4D sismic impedances in one or more regions, etc.). A reservoir simulation is a technique that makes it possible to simulate the flows of fluids within a reservoir with software called a “flow simulator.”
For this, the integration of all the available data is essential. These data generally comprise:                measurements, at certain points of the geological formation, of the property being modeled, for example in wells. These data are called static because they do not vary in time (in the timescale of the production of the reservoir) and are directly linked to the property of interest.        “history data”, comprising production data, for example the fluid flow rates measured on the wells, the concentrations of tracers and data obtained from sismic acquisition campaigns repeated at successive times. These data are called dynamic because they change during exploitation and are directly linked to the properties assigned to the mesh cells of the reservoir model.        
The available static data are used to define random functions for each petrophysical property such as the porosity or the permeability. A representation of the spatial distribution of a petrophysical property is a realization of a random function. Generally, a realization is generated from, on the one hand, a mean, a variance and a covariance function which characterizes the spatial variability of the property being studied and, on the other hand, from a seed or from a series of random numbers. There are numerous simulation techniques in existence, such as the Gaussian sequential simulation method, the Cholesky method or even the FFT-MA (Fast Fourier transform with moving average) method. The following documents describe such methods:    Goovaerts, P., 1997, Geostatistics for Natural Resources Evaluation, Oxford Press, New York, 483 p.    Le Ravalec, M., Noetinger B., and Hu L.-Y., 2000, The FFT Moving Average (FFT-MA) Generator: An Efficient Numerical Method for Generating and Conditioning Gaussian Simulations, Mathematical Geology, 32(6), 701-723.
The techniques of integrating dynamic data (production and/or 4D sismic) in a reservoir model are well known to the experts: these are so-called “history-matching” techniques. History matching modifies the parameters of a reservoir model, such as the permeabilities, the porosities or the skins of wells (representing the damage around the well), the fault connections, and so on, to minimize the deviations between the measured history data and the corresponding responses simulated on the basis of the model by a flow simulator. The parameters may be linked to geographic regions such as the permeabilities or porosities around one or more wells. The deviation between the actual data and simulated responses forms a function, called objective function. The problem of history matching is resolved by minimizing this function. Reservoir model perturbation techniques make it possible to modify a realization of a random function while ensuring the fact that the perturbed realization is also a realization of this same random function. Perturbation techniques that can be cited include the pilot points method developed by RamaRao et al. (1995) and Gomez-Hernandez et al. (1997), the gradual deformations method proposed by Hu (2000) and the probability perturbation method introduced by Caers (2003). These methods make it possible to modify the spatial distribution of the heterogeneities:    RamaRao, B. S, Lavenue, A. M. Marsilly, G. de, Marietta, M. G., 1995, Pilot Point Methodology for Automated Calibration of An Ensemble of Conditionally Simulated Transmissivity Fields. 1. Theory and Computational Experiments. WRR, 31(3), 475-493.    Gomez-Hernandez, J., Sahuquillo, A., and Capilla, J. E., 1997, Stochastic Simulation of Transmissivity Fields Conditional to Both Transmissivity and Piezometric Data, 1. Theory, J. of Hydrology, 203, 162-174.    Hu, L-Y., 2000, Gradual Deformation and Iterative Calibration of Gaussian-Related Stochastic Models, Math. Geol., 32(1), 87-108.    Caers, J., 2003, Geostatistical History Matching Under Training Image Based Geological Constraints. SPE J. 8(3), 218-226.
The geological formations are very heterogeneous as a result of complex formation phenomena combining migration, erosion and deposition processes. It is thus possible to observe, on a large scale, beds, troughs, lobes, bars, meanders, and so on. Even within these geological objects, signs of heterogeneity are also manifest with, for example, lateral accretion phenomena or bundles with oblique layering. At the rock sample scale, the permeability varies also because of changes in the level of the contacts between grains, the size of the grains, the density of the cracks, and so on. Moreover, the static and dynamic data used to constrain the geological model are associated with different scales and with different levels of resolution. The measurements on samples make it possible to appreciate the heterogeneity to sub-centimetric scale, the diagraphies to the scale of ten or so centimeters and the production data to a scale comparable to the distance between wells. Consequently, to be representative of the geological reservoir, it is advantageous to use multiple-scale parameterization techniques, which makes it possible to make modifications to the geological model at different scales matched to the level of resolution of the information to be incorporated.
A first approach has been proposed to address this need. It involves the following steps: the generation of a fine-scale geological model, an “upscaling” of this model resulting in a coarse-scale reservoir model, the flow simulations and the resolution of the coarse-scale inversion problem, and finally a step of down-scaling, to transform the coarse reservoir model into a fine geological model. Such a method is described in the document:    Tran, T. T., Wen, X.-H., Behrens, R. A., 1999, Efficient Conditioning of 3D Fine-Scale Reservoir Model to Multiphase Production Data Using Streamline-Based Coarse-Scale Inversion and Geostatistical Downscaling, SPE ATCE, Houston, Tex., USA, SPE 56518.
Two points must be stressed. First of all, the process of scaling the permeabilities from the fine model to the coarse model, also called “upscaling”, is based on an arithmetic mean, which is unsuited to permeabilities. Then, the coarse-scale matching step entails generating coarse-scale models which is done by considering an approximate variogram to describe the spatial variability of the permeability. It will be noted that the final step of downscaling from the coarse scale to the fine scale is performed either on the basis of a bayesienne approach, or on the basis of the Gaussian sequential simulation algorithm combined with cokriging. The major drawback with this method is that the fine-scale model obtained after matching is not in line with the dynamic data.
An interleaved matching approach has been put forward to correct this effect. This approach is described in the following document:    Aanonsen, S. I., Eydinova, D., 2006, A Multiscale Method for Distributed Parameter Estimation with Application to Reservoir History Matching, Comput. Geosci., 10, 97-117.
The model obtained after downscaling then has to be subjected to a new matching phase. There is then a first coarse-scale matching step with coarse-scale flow simulations and a second fine-scale matching step with fine-scale flow simulations. Consequently, the simulation time needed for this method is high. As for the works previously mentioned, the upscaling is performed on the basis of an arithmetic mean, which is inappropriate for the permeability for example, whereas the downscaling involves either an interpolation or a cokriging. Furthermore, all the proposed approaches are applied only to continuous petrophysical properties. They do not address the problem of matching facies models in view of facies being a discrete property.
The invention relates to a method for exploiting a geological reservoir according to an exploitation scheme defined on the basis of a reservoir model. The reservoir model is constructed and parameterized for at least two different scales, the finest-scale reservoir models (with the most mesh cells) being constrained by the coarsest-scale reservoir models (with the fewest mesh cells). Thus, the data can be integrated at different scales according to the levels of resolution involved and the constructed model is consistent in as much as the physical consistency between the fine geological models and the coarse reservoir models is preserved. Furthermore, the matching process is equally suited to continuous properties and discrete properties. The method is also economical, in as much as the number of parameters to be modified to incorporate the data can be considerably reduced relative to the number of mesh cells of the fine geological model, which brings about a significant limitation on the calls to the flow simulator and therefore a saving in terms of computation time.